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  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  from distutils.version import LooseVersion
  
  import numpy as np
  import pandas as pd
  
  import pyspark.pandas as ps
  from pyspark.pandas.window import Expanding
  from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
  
  
  class ExpandingTest(PandasOnSparkTestCase, TestUtils):
      def _test_expanding_func(self, f):
          pser = pd.Series([1, 2, 3], index=np.random.rand(3))
          psser = ps.from_pandas(pser)
          self.assert_eq(getattr(psser.expanding(2), f)(), getattr(pser.expanding(2), f)())
          self.assert_eq(
              getattr(psser.expanding(2), f)().sum(), getattr(pser.expanding(2), f)().sum()
          )
  
          # Multiindex
          pser = pd.Series(
              [1, 2, 3], index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
          )
          psser = ps.from_pandas(pser)
          self.assert_eq(getattr(psser.expanding(2), f)(), getattr(pser.expanding(2), f)())
  
          pdf = pd.DataFrame(
              {"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]}, index=np.random.rand(4)
          )
          psdf = ps.from_pandas(pdf)
          self.assert_eq(getattr(psdf.expanding(2), f)(), getattr(pdf.expanding(2), f)())
          self.assert_eq(getattr(psdf.expanding(2), f)().sum(), getattr(pdf.expanding(2), f)().sum())
  
          # Multiindex column
          columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
          pdf.columns = columns
          psdf.columns = columns
          self.assert_eq(getattr(psdf.expanding(2), f)(), getattr(pdf.expanding(2), f)())
  
      def test_expanding_error(self):
          with self.assertRaisesRegex(ValueError, "min_periods must be >= 0"):
              ps.range(10).expanding(-1)
  
          with self.assertRaisesRegex(
              TypeError, "psdf_or_psser must be a series or dataframe; however, got:.*int"
          ):
              Expanding(1, 2)
  
      def test_expanding_repr(self):
          self.assertEqual(repr(ps.range(10).expanding(5)), "Expanding [min_periods=5]")
  
      def test_expanding_count(self):
          # The behaviour of Expanding.count are different between pandas>=1.0.0 and lower,
          # and we're following the behaviour of latest version of pandas.
          if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
              self._test_expanding_func("count")
          else:
              # Series
              idx = np.random.rand(3)
              psser = ps.Series([1, 2, 3], index=idx, name="a")
              expected_result = pd.Series([None, 2.0, 3.0], index=idx, name="a")
              self.assert_eq(psser.expanding(2).count().sort_index(), expected_result.sort_index())
              self.assert_eq(psser.expanding(2).count().sum(), expected_result.sum())
  
              # MultiIndex
              midx = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
              psser = ps.Series([1, 2, 3], index=midx, name="a")
              expected_result = pd.Series([None, 2.0, 3.0], index=midx, name="a")
              self.assert_eq(psser.expanding(2).count().sort_index(), expected_result.sort_index())
  
              # DataFrame
              psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
              expected_result = pd.DataFrame({"a": [None, 2.0, 3.0, 4.0], "b": [None, 2.0, 3.0, 4.0]})
              self.assert_eq(psdf.expanding(2).count().sort_index(), expected_result.sort_index())
              self.assert_eq(psdf.expanding(2).count().sum(), expected_result.sum())
  
              # MultiIndex columns
              idx = np.random.rand(4)
              psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]}, index=idx)
              psdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
              expected_result = pd.DataFrame(
                  {("a", "x"): [None, 2.0, 3.0, 4.0], ("a", "y"): [None, 2.0, 3.0, 4.0]},
                  index=idx,
              )
              self.assert_eq(psdf.expanding(2).count().sort_index(), expected_result.sort_index())
  
      def test_expanding_min(self):
          self._test_expanding_func("min")
  
      def test_expanding_max(self):
          self._test_expanding_func("max")
  
      def test_expanding_mean(self):
          self._test_expanding_func("mean")
  
      def test_expanding_sum(self):
          self._test_expanding_func("sum")
  
      def test_expanding_std(self):
          self._test_expanding_func("std")
  
      def test_expanding_var(self):
          self._test_expanding_func("var")
  
      def _test_groupby_expanding_func(self, f):
          pser = pd.Series([1, 2, 3, 2], index=np.random.rand(4), name="a")
          psser = ps.from_pandas(pser)
          self.assert_eq(
              getattr(psser.groupby(psser).expanding(2), f)().sort_index(),
              getattr(pser.groupby(pser).expanding(2), f)().sort_index(),
          )
          self.assert_eq(
              getattr(psser.groupby(psser).expanding(2), f)().sum(),
              getattr(pser.groupby(pser).expanding(2), f)().sum(),
          )
  
          # Multiindex
          pser = pd.Series(
              [1, 2, 3, 2],
              index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("c", "z")]),
              name="a",
          )
          psser = ps.from_pandas(pser)
          self.assert_eq(
              getattr(psser.groupby(psser).expanding(2), f)().sort_index(),
              getattr(pser.groupby(pser).expanding(2), f)().sort_index(),
          )
  
          pdf = pd.DataFrame({"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]})
          psdf = ps.from_pandas(pdf)
          self.assert_eq(
              getattr(psdf.groupby(psdf.a).expanding(2), f)().sort_index(),
              getattr(pdf.groupby(pdf.a).expanding(2), f)().sort_index(),
          )
          self.assert_eq(
              getattr(psdf.groupby(psdf.a).expanding(2), f)().sum(),
              getattr(pdf.groupby(pdf.a).expanding(2), f)().sum(),
          )
          self.assert_eq(
              getattr(psdf.groupby(psdf.a + 1).expanding(2), f)().sort_index(),
              getattr(pdf.groupby(pdf.a + 1).expanding(2), f)().sort_index(),
          )
          self.assert_eq(
              getattr(psdf.b.groupby(psdf.a).expanding(2), f)().sort_index(),
              getattr(pdf.b.groupby(pdf.a).expanding(2), f)().sort_index(),
          )
          self.assert_eq(
              getattr(psdf.groupby(psdf.a)["b"].expanding(2), f)().sort_index(),
              getattr(pdf.groupby(pdf.a)["b"].expanding(2), f)().sort_index(),
          )
          self.assert_eq(
              getattr(psdf.groupby(psdf.a)[["b"]].expanding(2), f)().sort_index(),
              getattr(pdf.groupby(pdf.a)[["b"]].expanding(2), f)().sort_index(),
          )
  
          # Multiindex column
          columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
          pdf.columns = columns
          psdf.columns = columns
          self.assert_eq(
              getattr(psdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
              getattr(pdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
          )
  
          self.assert_eq(
              getattr(psdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
              getattr(pdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
          )
  
      def test_groupby_expanding_count(self):
          # The behaviour of ExpandingGroupby.count are different between pandas>=1.0.0 and lower,
          # and we're following the behaviour of latest version of pandas.
          if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
              self._test_groupby_expanding_func("count")
          else:
              # Series
              psser = ps.Series([1, 2, 3, 2], index=np.random.rand(4))
              midx = pd.MultiIndex.from_tuples(
                  list(zip(psser.to_pandas().values, psser.index.to_pandas().values))
              )
              expected_result = pd.Series([np.nan, np.nan, np.nan, 2], index=midx)
              self.assert_eq(
                  psser.groupby(psser).expanding(2).count().sort_index(), expected_result.sort_index()
              )
              self.assert_eq(psser.groupby(psser).expanding(2).count().sum(), expected_result.sum())
  
              # MultiIndex
              psser = ps.Series(
                  [1, 2, 3, 2],
                  index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("a", "y")]),
              )
              midx = pd.MultiIndex.from_tuples(
                  [(1, "a", "x"), (2, "a", "y"), (3, "b", "z"), (2, "a", "y")]
              )
              expected_result = pd.Series([np.nan, np.nan, np.nan, 2], index=midx)
              self.assert_eq(
                  psser.groupby(psser).expanding(2).count().sort_index(), expected_result.sort_index()
              )
  
              # DataFrame
              psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
              midx = pd.MultiIndex.from_tuples([(1, 0), (2, 1), (2, 3), (3, 2)], names=["a", None])
              expected_result = pd.DataFrame(
                  {"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]}, index=midx
              )
              self.assert_eq(
                  psdf.groupby(psdf.a).expanding(2).count().sort_index(), expected_result.sort_index()
              )
              self.assert_eq(psdf.groupby(psdf.a).expanding(2).count().sum(), expected_result.sum())
              expected_result = pd.DataFrame(
                  {"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]},
                  index=pd.MultiIndex.from_tuples(
                      [(2, 0), (3, 1), (3, 3), (4, 2)], names=["a", None]
                  ),
              )
              self.assert_eq(
                  psdf.groupby(psdf.a + 1).expanding(2).count().sort_index(),
                  expected_result.sort_index(),
              )
              expected_result = pd.Series([None, None, 2.0, None], index=midx, name="b")
              self.assert_eq(
                  psdf.b.groupby(psdf.a).expanding(2).count().sort_index(),
                  expected_result.sort_index(),
              )
              self.assert_eq(
                  psdf.groupby(psdf.a)["b"].expanding(2).count().sort_index(),
                  expected_result.sort_index(),
              )
              expected_result = pd.DataFrame({"b": [None, None, 2.0, None]}, index=midx)
              self.assert_eq(
                  psdf.groupby(psdf.a)[["b"]].expanding(2).count().sort_index(),
                  expected_result.sort_index(),
              )
  
              # MultiIndex column
              psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
              psdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
              midx = pd.MultiIndex.from_tuples(
                  [(1, 0), (2, 1), (2, 3), (3, 2)], names=[("a", "x"), None]
              )
              expected_result = pd.DataFrame(
                  {"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]}, index=midx
              )
              expected_result.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
              self.assert_eq(
                  psdf.groupby(("a", "x")).expanding(2).count().sort_index(),
                  expected_result.sort_index(),
              )
              midx = pd.MultiIndex.from_tuples(
                  [(1, 4.0, 0), (2, 1.0, 3), (2, 2.0, 1), (3, 3.0, 2)],
                  names=[("a", "x"), ("a", "y"), None],
              )
              expected_result = pd.DataFrame(
                  {
                      ("a", "x"): [np.nan, np.nan, np.nan, np.nan],
                      ("a", "y"): [np.nan, np.nan, np.nan, np.nan],
                  },
                  index=midx,
              )
              self.assert_eq(
                  psdf.groupby([("a", "x"), ("a", "y")]).expanding(2).count().sort_index(),
                  expected_result.sort_index(),
              )
  
      def test_groupby_expanding_min(self):
          self._test_groupby_expanding_func("min")
  
      def test_groupby_expanding_max(self):
          self._test_groupby_expanding_func("max")
  
      def test_groupby_expanding_mean(self):
          self._test_groupby_expanding_func("mean")
  
      def test_groupby_expanding_sum(self):
          self._test_groupby_expanding_func("sum")
  
      def test_groupby_expanding_std(self):
          self._test_groupby_expanding_func("std")
  
      def test_groupby_expanding_var(self):
          self._test_groupby_expanding_func("var")
  
  
  if __name__ == "__main__":
      import unittest
      from pyspark.pandas.tests.test_expanding import *  # noqa: F401
  
      try:
          import xmlrunner  # type: ignore[import]
  
          testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
      except ImportError:
          testRunner = None
      unittest.main(testRunner=testRunner, verbosity=2)
  